Economic and financial modeling and planning is commonly used to estimate or predict the performance and outcome of real systems, given specific sets of input data of interest. A model is a mathematical expression or representation which predicts the outcome or behavior of the system under a variety of conditions. In one sense, it is relatively easy, in the past tense, to review historical data, understand its past performance, and state with relative certainty that the system's past behavior was indeed driven by the historical data. A much more difficult task, but one that is extremely valuable, is to generate a mathematical model of the system which predicts how the system will behave, or would have behaved, with different sets of data and assumptions. While forecasting and backcasting using different sets of input data is inherently imprecise, i.e., no model can achieve 100% certainty, the field of probability and statistics has provided many tools which allow such predictions to be made with reasonable certainty and acceptable levels of confidence.
In its basic form, the economic model can be viewed as a predicted or anticipated outcome of a mathematical expression, as driven by a given set of input data and assumptions. The input data is processed through the mathematical expression representing either the expected or current behavior of the real system. The mathematical expression is formulated or derived from principles of probability and statistics, often by analyzing historical data and corresponding known outcomes, to achieve a best fit of the expected behavior of the system to other sets of data, both in terms of forecasting and backcasting. In other words, the model should be able to predict the outcome or response of the system to a specific set of data being considered or proposed, within a level of confidence, or an acceptable level of uncertainty.
Economic-based models have numerous variables and influences which determine its behavior. For example, in the case of home equity loans and lines of credit, some common rules are: (1) maximum rate change per cell not to exceed predefined limit per pricing cycle; (2) maximum rate not to exceed predefined values; (3) no price differentiation by channel; (4) no price differentiation between 2nd and 3rd liens; (5) for fixed rate products, rates have a consistent gap between FICO (Fair Isaac Corporation) and term tiers within a dollar tier; (6) no rate differentiation between home equity loan prices and fully amortizing fix rate loan option prices (for similar parameters); (7) each product cell has a positive net present value of performance; (8) each product cell has a risk-adjusted return on capital not lower than a predefined level; and (9) portfolio of home equity line of credit for 2nd liens have a minimum return on tangible equity(ROTE) of predefined level and a minimum risk-adjusted return on capital of a predefined value.
For an accurate model, these business rules must be considered during the optimization cycle. In doing so, the first problem encountered is how to describe and build an intelligent network that maps multitude of rules spanning millions of rate cells into a minimally defined structure communicating to the optimization system. Often there is also a severe scarcity of available information at the rate cell level to base an optimized rate recommendation independent of whether the product contributes to the financial institution's key performance indicators.
A need exists for a method to effectively model a wide class of business rules, allowing financial institutions to maintain a multitude of rate cells while optimizing their key performance indicators.